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基于改进谱哈希的大规模图像检索 被引量：3

A large-scale image retrieval method based on improved spectral hashing
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摘要 为了提高图像检索精度,文章在谱哈希的基础上引入最小量化误差的思想,提出了一种基于改进谱哈希的大规模图像检索算法,该算法避免了谱哈希中要求的数据服从均匀分布的假设,并且能够保持数据在原始空间的相似性;引入Boosting算法来确定阈值,使得该算法具有更强的适应性和更广泛的应用;在公开的图像数据集上做了实验,实验结果表明该方法优于谱哈希、局部敏感哈希和迭代量化等哈希算法。 In this paper, a large-scale image retrieval method based on improved spectral hashing is proposed by introducing the quantization error minimization to improve the image retrieval accuracy. The algorithm gets rid of the assumption that data is uniformly distributed required by the spectral hashing, and can keep the data similarity in the original space. Boosting algorithm is used to determine the threshold so that the algorithm can be more adaptive and more widely used. The algorithm is evaluated on the public datasets, and the experimental results show that the proposed method is better than spectral hashing（SH）, locality-sensitive hashing （LSH） and iterative quantization hashing algorithm.
作者 夏立超 蒋建国 齐美彬
机构地区 合肥工业大学计算机与信息学院
出处 《合肥工业大学学报（自然科学版）》 CAS CSCD 北大核心 2016年第8期1049-1054,共6页 Journal of Hefei University of Technology：Natural Science
基金 国家自然科学基金资助项目(61371155 61174170)
关键词 哈希 经验误差 拉普拉斯矩阵 BOOSTING算法 hashing empirical error Laplacian matrix Boosting algorithm
分类号 TN919.81 [电子电信—通信与信息系统]
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参考文献23

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二级参考文献14

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共引文献2

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引证文献3

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合肥工业大学学报（自然科学版）
2016年 第8期

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